Data augmentation experiments.
For the experiments with DeepAugment [1]}, we generate distorted images once before training using the CAE model from their public repository.
For the experiments that involve training on Stylized ImageNet, we use in each batch 28 stylized images and 100 standard ImageNet images following [2]}.
ANT [2]} is the only exception where instead of training from a random initialization, we follow the scheme of the authors and fine-tune a standardly pretrained ImageNet-100 model.
Additionally, we note that the noise generator of [2]} uses skip connections with Gaussian noise and experience replay of previous noise generators.
This means that there is a certain Gaussian noise component in the final noise which implies that it partially overlaps with the common corruptions from CIFAR-10-C and ImageNet-100-C.
